Prediction of Long-term Deposit Customers Using SVM Optimized with Borderline-SMOTE

Yunyi Gao

Proceedings of the 3rd International Conference on Mathematical Statistics and Economic Analysis, MSEA 2024, May 24–26, 2024, Jinan, China

Research Article

Prediction of Long-term Deposit Customers Using SVM Optimized with Borderline-SMOTE

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Cite: BibTeX Plain Text

@INPROCEEDINGS{10.4108/eai.24-5-2024.2350156,
    author={Yunyi  Gao},
    title={Prediction of Long-term Deposit Customers Using SVM Optimized with Borderline-SMOTE},
    proceedings={Proceedings of the 3rd International Conference on Mathematical Statistics and Economic Analysis, MSEA 2024, May 24--26, 2024, Jinan, China},
    publisher={EAI},
    proceedings_a={MSEA},
    year={2024},
    month={10},
    keywords={economic situation; long-term deposits; capital stability; machine learning},
    doi={10.4108/eai.24-5-2024.2350156}
}

Yunyi Gao
Year: 2024
Prediction of Long-term Deposit Customers Using SVM Optimized with Borderline-SMOTE
MSEA
EAI
DOI: 10.4108/eai.24-5-2024.2350156

Yunyi Gao¹^,*

1: Nanjing Agricultural University, Nanjing, China

*Contact email: 2911458297@qq.com

Abstract

As the economic situation and market environments continually evolve, the financial sector faces numerous challenges, one of which is effectively enhancing the capacity to attract long-term deposits. Long-term deposits are crucial for the stability of a bank's capital and are also a critical factor in enabling banks to offer loans at lower costs. This paper aims to identify existing customers who are highly likely to subscribe to long-term deposits by employing machine learning techniques, thereby helping banks to more accurately target their marketing strategies and improve the efficiency of resource utilization.

Keywords: economic situation; long-term deposits; capital stability; machine learning

Published: 2024-10-01
Publisher: EAI

: http://dx.doi.org/10.4108/eai.24-5-2024.2350156